https://github.com/dmis-lab/bern2
공식 코드BERN2: an advanced neural biomedical named entity recognition and normalization tool
Papers with Code 아카이브
Named Entity Recognition (NER)
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Named Entity Recognition (NER) is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.
Example:
| Mark | Watney | visited | Mars |
|---|---|---|---|
| B-PER | I-PER | O | B-LOC |
<span style="color:grey; opacity: 0.6">( Image credit: Zalando )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dmis-lab/biobert
공식 코드BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/dmis-lab/bioner-generalization
공식 코드How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?
https://github.com/dmis-lab/conner
공식 코드Enhancing Label Consistency on Document-level Named Entity Recognition
https://github.com/dmis-lab/gener
공식 코드https://github.com/dmlc/dgl
공식 코드https://github.com/dongguanting/msdp-fewshot-ner
공식 코드A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
https://github.com/dorringel/SWVM
공식 코드https://github.com/dpressel/mead-baseline
공식 코드Computationally Efficient NER Taggers with Combined Embeddings and Constrained Decoding
https://github.com/dreamyang-liu/SciAnnotate
공식 코드SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling
https://github.com/drgriffis/NeuralVecmap
공식 코드https://github.com/druv022/Disease-Normalization-with-Graph-Embeddings
공식 코드https://github.com/ds4dh/GLiNER-biomed
공식 코드GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition
https://github.com/ds4dh/jnrf
공식 코드https://github.com/dsfsi/puoberta
공식 코드PuoBERTa: Training and evaluation of a curated language model for Setswana
https://github.com/dsfsi/puodata
공식 코드PuoBERTa: Training and evaluation of a curated language model for Setswana
https://github.com/dugu9sword/LEMON
공식 코드Chinese Named Entity Recognition Augmented with Lexicon Memory
https://github.com/dumitrescustefan/ronec
공식 코드https://github.com/dwadden/dygiepp
공식 코드Entity, Relation, and Event Extraction with Contextualized Span Representations
https://github.com/edchengg/easyproject
공식 코드Frustratingly Easy Label Projection for Cross-lingual Transfer
https://github.com/edchengg/gollie-transfusion
공식 코드Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction
https://github.com/elangovana/nlp-train-test-overlap-detector
공식 코드Memorization vs. Generalization: Quantifying Data Leakage in NLP Performance Evaluation
https://github.com/elenamer/noisebench
공식 코드NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition
https://github.com/elenanereiss/Legal-Entity-Recognition
공식 코드A Dataset of German Legal Documents for Named Entity Recognition
https://github.com/emanuelaboros/ocr-sensitive-neurons
공식 코드Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical Documents
https://github.com/emma1066/retrieval-augmented-it-openner
공식 코드Retrieval Augmented Instruction Tuning for Open NER with Large Language Models
https://github.com/emma1066/zero-shot-ner-with-chatgpt
공식 코드https://github.com/emorynlp/elit
공식 코드https://github.com/emorynlp/seq2seq-corenlp
공식 코드https://github.com/emorynlp/stem-cell-hypothesis
공식 코드The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders
https://github.com/emseoyk/verifiner
공식 코드VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models
https://github.com/eoduself/udl
공식 코드Link, Synthesize, Retrieve: Universal Document Linking for Zero-Shot Information Retrieval
https://github.com/espnet/espnet
공식 코드https://github.com/ethch18/specializing-multilingual
공식 코드Specializing Multilingual Language Models: An Empirical Study
https://github.com/eulring/dmner
공식 코드DMNER: Biomedical Entity Recognition by Detection and Matching
https://github.com/evrog/russian-qa-jeopardy
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.